Development of an in-home, real-time air pollutant sensor platform and implications for community use. (January 2019) - Gillooly, Sara E.; Zhou, Yulun; Vallarino, Jose; Chu, MyDzung T.; Michanowicz, Drew R.; Levy, Jonathan I.; Adamkiewicz, Gary
Abstract: Air pollution exposure characterization has been shaped by many constraints. These include technologies that lead to insufficient coverage across space and/or time in order to characterize individual or community-level exposures with sufficient accuracy and precision. However, there is now capacity for continuous monitoring of many air pollutants using comparatively inexpensive, real-time sensors. Crucial questions remain regarding whether or not these sensors perform adequately for various potential end uses and whether performance varies over time or across ambient conditions. Performance scrutiny of sensors via lab- and field-testing and calibration across their lifetime is necessary for interpretation of data, and has important implications for end users including cost effectiveness and ease of use. We developed a comparatively lower-cost, portable, in-home air sampling platform and a guiding development and maintenance workflow that achieved our goal of characterizing some key indoor pollutants with high sensitivity and reasonable accuracy. Here we describe the process of selecting, validating, calibrating, and maintaining our platform – the Environmental Multi-pollutant Monitoring Assembly (EMMA) – over the course of our study to-date. We highlight necessary resources and consider implications for communities or researchers interested in developing such platforms, focusing on PM2.5, NO, and NO2 sensors. Our findings emphasize that lower-cost sensors should be deployed with caution, given financial and resource costs that greatly exceed sensor costs, but that selected community objectives could be supported at lesser cost and community-based participatory research strategies could be used for more wide-ranging goals. Graphical abstract: Image 1 Highlights: We present a framework for maintaining an in-home air pollution sensor platform. Achieving quality low-cost sensor data requires resources inaccessible to communities. Costs of sensor maintenance are low upfront but increase with study duration. Collaboration between scientific and non-scientific groups can produce optimal data.